Person: Callejas Bedregal, Benjamin
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Callejas Bedregal
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Benjamin
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IngenierĆa ElĆ©ctrica, ElectrĆ³nica y de ComunicaciĆ³n
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0000-0002-6757-7934
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811677
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Publication Open Access d-Choquet integrals: Choquet integrals based on dissimilarities(Elsevier, 2020) Bustince Sola, Humberto; Mesiar, Radko; FernĆ”ndez FernĆ”ndez, Francisco Javier; Galar Idoate, Mikel; Paternain Dallo, Daniel; Altalhi, A. H.; Pereira Dimuro, GraƧaliz; Callejas Bedregal, Benjamin; TakĆ”Ä, Zdenko; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; EstadĆstica, InformĆ”tica y MatemĆ”ticas; Universidad PĆŗblica de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA13The paper introduces a new class of functions from [0,1]n to [0,n] called d-Choquet integrals. These functions are a generalization of the 'standard' Choquet integral obtained by replacing the difference in the definition of the usual Choquet integral by a dissimilarity function. In particular, the class of all d-Choquet integrals encompasses the class of all 'standard' Choquet integrals but the use of dissimilarities provides higher flexibility and generality. We show that some d-Choquet integrals are aggregation/pre-aggregation/averaging/functions and some of them are not. The conditions under which this happens are stated and other properties of the d-Choquet integrals are studied.Publication Embargo Non-symmetric over-time pooling using pseudo-grouping functions for convolutional neural networks(Elsevier, 2024) Ferrero Jaurrieta, Mikel; Paiva, Rui; Cruz, Anderson; Callejas Bedregal, Benjamin; Miguel Turullols, Laura de; TakĆ”Ä, Zdenko; LĆ³pez Molina, Carlos; Bustince Sola, Humberto; EstadĆstica, InformĆ”tica y MatemĆ”ticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISCConvolutional Neural Networks (CNNs) are a family of networks that have become state-of-the-art in several fields of artificial intelligence due to their ability to extract spatial features. In the context of natural language processing, they can be used to build text classification models based on textual features between words. These networks fuse local features to generate global features in their over-time pooling layers. These layers have been traditionally built using the maximum function or other symmetric functions such as the arithmetic mean. It is important to note that the order of input local features is significant (i.e. the symmetry is not an inherent characteristic of the model). While this characteristic is appropriate for image-oriented CNNs, where symmetry might make the network robust to image rigid transformations, it seems counter-productive for text processing, where the order of the words is certainly important. Our proposal is, hence, to use non-symmetric pooling operators to replace the maximum or average functions. Specifically, we propose to perform over-time pooling using pseudo-grouping functions, a family of non-symmetric aggregation operators that generalize the maximum function. We present a construction method for pseudo-grouping functions and apply different examples of this family to over-time pooling layers in text-oriented CNNs. Our proposal is tested on seven different models and six different datasets in the context of engineering applications, e.g. text classification. The results show an overall improvement of the models when using non-symmetric pseudo-grouping functions over the traditional pooling function.Publication Open Access Generalization of QL-operators based on general overlap and general grouping functions(IEEE, 2022) Botelho, Cecilia; Galvao, Alessandra; Santos, Helida; Pinheiro, Jocivania; Callejas Bedregal, Benjamin; Yamin, Adenauer; Reiser, Renata; EstadĆstica, InformĆ”tica y MatemĆ”ticas; Estatistika, Informatika eta MatematikaFirstly, this work discusses the main conditions guarantying that general overlap (grouping) functions can be obtained from n-dimensional overlap (grouping) functions. Focusing on QL-implications, which are usually generated by strong negations together with t-norms and t-conorms, we consider a non-restrictive construction, by relaxing not only the associativity and the corresponding neutral elements (NE) but also the reverse construction of other properties. Thus, the main properties of the QL-implication class are studied, considering a tuple (G,N,O) generated from grouping and overlap functions together with the greatest fuzzy negation. In addition, in order to provide more flexibility, we define a subclass of QL-implications generated from general overlap and general grouping functions. Some examples are introduced, illustrating the constructive methods to generate such operators.